The approach is a direct extension of the incremental algorithm (Dale, 1995).

this work only provides a proof of concept example to illustrate the idea.


A directed hypergraph G (Gallo et al., 1993) is a tuple of the form: Similar to regular graphs, a hypergraph consists of a set of nodes X and a set of arcs A.

different from regular graphs, each arc in A is considered as a hyperarc in the sense that it can capture relations between any two subsets of nodes: a tail (t i ) and a head (h i ).


On the one hand, we did not have a large set of human descriptions of the impoverished scene to learn the stochastic cost.

it is not clear whether human strategies of describing the impoverished scene should be used to represent optimal strategies for the robot.


This seems to indicate that exploring extra effort in REG could help mediate mismatched perceptions in situated dialogue.

more understanding on how to engage in such extra effort will be required in the future.


1.00 1.00 0.80 0.30 fwd sports cars 1.00 1.00 1.00 1.00 garden landscaping magazines 0.00 0.10 0.15 0.06 heliskiing resorts 1.00 1.00 1.00 1.00 hell in a cell wrestlers 1.00 1.00 1.00 0.92 holidays celebrated in sydney  Table 7: Precision at various ranks in the ranked lists of instances extracted from queries, for various target class labels and as an average over the entire set of 50 target class labels lore" and "daft punk live albums", and especially for "garden landscaping magazines" which has the worst precision.

instances extracted for "companies with sustainable competitive advantage" or "criminals who have been executed" have high precision across all ranks.


(Bunescu and Pasca, 2006) propose to generalize beyond context-entity correlation s(d, e) with word-category correlation s(w, c).

this method works at word level, and does not scale well to large number of categories.


(Cucerzan, 2007) first suggests to optimize an objective function that is similar to the collective ap-proach.

the author adopts an approximation method because of the large search space (which is O(n m ) for a document with m mentions, each with n candidates).


For example, consider the sentence: "It also marks P&G's growing concern that its Japanese rivals, such as Kao Corp., may bring their superconcentrates to the U.S." According to our annotation style, there is a relation "rivals" between "P&G" and "Kao Corp." in this sentence.

the original annotations for PENN-100 consider only the relation between "Kap Corp." and the pronoun "it", leaving the task of resolving the coreference between "P&G" and "it" as a posterior step.


Recently, a lot of work (Sarkas et al., 2010;Li et al., 2009) proposed the task of structured annotation of queries which aims to detect the structure of the query and assign a specific label to it.

to our knowledge, the previous methods do not exploit an effective approach for improving web search ranking by incorporating structured annotation of queries.


For the sake of simplicity, this paper assumes that attributes in domain schema are available.

it is not difficult to pre-specify attributes in a specific domain.


Figure 3(b) shows that the precision of the structured annotation is lowest when δ = 0 .

the precision is still as high as 0.7375, and the highest recall is obtained in this case.


Machine translation already has a decades long history and an array of commercial systems were already deployed, including Google Translate 1 and Systran 2 .

due to the intrinsic difficulty of the task, a number of related problems remain open, including: the gap between text semantics and statistically derived translations, the scarcity of resources in a large majority of languages and the quality of automatically obtained resources and translations.


This finding advocates for the importance of good quality generic dictionaries in specialized lexicon translation approaches.

it is clear that such dictionaries are far from being sufficient in order to cover all possible domains.


Most of the features are extracted based only on the sentence to be compressed.

we introduce a few document level features.


Increasing the order of our dominance model provides an additional gain.

the gain is more pronounced in the weblog genre (up to around 1 BLEU point) than in the newswire genre.


Even when the number of possible permutations is large we can limit ourselves to the K most popular permutations.

our approach provides important advantages.


With a triangulation pivot approach, a source-target phrase table can be obtained by combining the source-pivot phrase table and the pivot-target phrase table.

one of the weaknesses is that some corresponding source and target phrase pairs cannot be generated, because they are connected to different pivot phrases (Cui et al., 2013).


IHMM for system combination, HMM in GIZA++ software for statistical machine translation (SMT) (Och and Ney, 2000;Koehn et al., 2003), are designed for one-to-many alignment, and running GIZA++ from two directions to gain better performance turns into a standard operation in SMT, therefore we are seeking a way to empower IHMM by introducing bi-directional information.

it appears to be intractable in an IHMM model to search the optimal solution by simply defining a new goal as a product of probabilities from two directions.


The unnormalized conditional alignment probability is .

the same alignment (f 1 , e 1 )(f 1 , e 2 )(f 2 , e 3 ), if we change the alignment direction, the backbone being observations, would be a bit different.


A straightforward method to introduce awareness of source syntax to translation rules is to apply the same well-formed dependency constraint and head POS annotation on the target side of stringto-dependency translation rules to the source side.

as discussed earlier, this would significantly reduce the number of rules that can be extracted, exacerbate data sparsity, and cause other problems, especially given that the target side is already constrained by the dependency requirement.


Previous studies have exploited revision history data in tasks such as preposition error correction (Cahill et al., 2013), spelling error correction (Zesch, 2012) or paraphrasing (Max and Wisniewski, 2010).

they all use different approaches to extract the information needed for their task.


In edit category classification, we also have two documents.

these documents are different versions of the same text.


It is the most straightforward approach when dealing with multi-labeled data.

it does not consider possible relationships or dependencies between categories.


Or in the case of identical alignment, New York City aligning to New York City is simply New↔New, York↔York, City↔City.

it is not as clear how to token-align New York (as a city) with New York City.


Deng and Byrne (2008) explored token-to-phrase alignment based on HMM models (Vogel et al., 1996) by explicitly modeling the token-to-phrase probability and phrase lengths.

the token-to-phrase alignment is only in one direction: each target state still only spans one source word, and thus alignment on the source side is limited to tokens.


This strategy has been successful and commonly used in coreference resolution (Ng and Cardie, 2002;Bengtson and Roth, 2008;Stoyanov et al., 2009).

most works have developed ad-hoc approaches to implement this idea.


Another approach proposed by Yu and Joachims (2009) formu-lates coreference with latent spanning trees.

their approach has no directionality between mentions, whereas our latent structure captures the natural left-to-right ordering of mentions.


Performance on named entities: The coreference annotation in Ontonotes 5.0 includes various types of mentions.

not all mention types are equally interesting.


This is a practical difficulty of spectral methods, for example to apply evaluation metrics like perplexity which are only defined for distributions.

in the previous section we have exposed that if we have access to estimates of a Hankel matrix of a WCFG G, we can recover G. the statistics in the Hankel require access to strings that have information about context-free cuts.


One can prove that there is theoretical identifiability of the rank and the parameters of an FST distribution, using a rank minimization formulation.

this problem is NP-hard, and it remains open whether there exists a polynomial method with identifiability results.


Previous studies show that joint solutions usually lead to the improvement in accuracy over pipelined systems by exploiting POS information to help word segmentation and avoiding error propagation.

traditional joint approaches usually involve a great number of features, which arises four limitations.


(Gao et al., 2004) described a transformationbased converter to transfer a certain annotationstyle word segmentation result to another style.

this converter need human designed transformation templates, and is hard to be generalized to POS tagging.


Traditionally, a popular lexical database of English is Wordnet (Miller, 1995;Miller and Fellbaum, 1998), which organizes the semantic network in terms of graph theory.

to manual approaches, the automatic analysis of semantically interesting graph structures of language has received increasing attention.


Our system clusters relations with similar named-entity arguments, but this means it does not cluster relations whose arguments are rarely named entities.

using crosslingual clusters of common nouns, such as those from Täckström et al.


Table 3: Five sarcastic tweets found by the Contrast method but not the SVM These tweets are good examples of a positive sentiment (love, enjoy, awesome, can't wait) contrasting with a negative situation.

the negative situation phrases are not always as specific as they should be.


Some false hits come from situations that are frequently negative but not always negative (e.g., some people genuinely like waking up early).

most false hits were due to overly general negative situation phrases (e.g., "I love working there" was labeled as sarcastic).


Among these fields, the Overview is required and the others are optional, such as Project, Course and Interest groups.

compared with Overview, Summary, Experience, Education fields, they seem to be less important for summarization of personal profiles.


MaxEnt 0.0349 CoFG 0.0383 CoFG-edu 0.0382 CoFG-exp 0.0381 Table 5: ROUGE-2 F-Measure score of the contribution of social edges From Table 5, we can see that all of our proposed approaches, i.e., CoFG-edu, CoFG-exp, and CoFG, outperform the baseline approach, i.e., MaxEnt.

the performance of CoFG-edu, CoFG-exp and CoFG are similar.


In most of the existing summarization systems, people need to first define a constant length to restrict all the output summaries.

in many cases it is improper to require all summaries are of the same length.


The Rouge evaluation requires golden standard summaries as the base.

in many cases we cannot get the reference summaries.


This is not an issue of concern to (Teufel and Moens, 2002), but relates to the notion of NUCLEUS and SATELLITE clauses, which form the foundation of Rhetorical Structure Theory (Mann and Thompson, 1998), and guides the summarisation paradigm of (Marcu, 1998a;Marcu, 1998b).

the difference here is that we define a-priori certain categories to be independent (have the property of playing the role of nucleus in the discourse) and specify their relation with particular types of dependent categories.


The experimental setup follows the paradigm of (Teufel, 2001).

while (Teufel, 2001) developed a Q-A task to evaluate summaries showing the contribution of a scientific article in relation to previous work, the purpose of the Q-A task at hand is to show the usefulness of the extracted summaries in answering questions on the paper, and how they compare to a discourse-agnostic baseline.


Because it needs to explore an exponentially large space in the worst case, a bounded priority queue becomes necessary to ensure limited parsing time.

huang and Sagae (2010) explore the idea of dynamic programming, which is originated in bottom-up constituent parsing algorithms like Earley (1970), but in a beam-based non best-first parser.


This requirement can be easily satisfied if we use a generative scoring model like PCFG.


1 The vanilla best-first parsing algorithm inherits the optimality directly from Dijkstra algorithm.

it explores exponentially many derivations to reach the goal configuration in the worst case.


We reach 92.39% accuracy with structured perceptron.

in experiments we still use MaxEnt to make the comparison fair.


DP best-first parser is as fast as non-DP for short sentences, but the time grows significantly slower.

it explores ∼17 times more states than DP, with an unbearable average time.


Figure 6 (a) shows that beam parser fails to reach the optimality, while exploring significantly more states.

beam parser also fails to reach an accuracy as high as best-first parsers.


The problem of learning language models from large text corpora has been widely studied within the computational linguistic community.

little is known about the performance of these language models when applied to the computer vision domain.


Indeed, the error sparsity makes it very challenging to identify mistakes accurately, and no system in the shared task achieves a precision over 50%.

once the precision drops below 50%, the system introduces more mistakes than it identifies.


To compensate this deficiency, we tried combining the three lexical resources in various ways (taking the union or combining them in a pipeline using the first resource that would yield a synonym).

the results did not improve and even in some cases worsened due probably to the insufficient lexical disambiguation.


One could argue that a joint model is more attractive as potential antecedents such as building "trigger" subsequent bridging cases such as stairs (Example 1).

bridging can be indicated by referential patterns without world knowledge about the anaphor/antecedent NPs, as the nonsense example 2 shows: the wug is clearly a bridging anaphor although we do not know the antecedent.


Bridging anaphora can have almost limitless variation.

we observe that bridging anaphors are often licensed because of discourse structure Markert et al.


This feature set (Table 1, f 1-f 13) works well to identify old, new and several mediated categories.

it fails to recognize most bridging anaphora which we try to remedy in this work by including more diverse features.


They apply an expectation-maximization approach to learn how words align to elements of the target grammar, and achieve performance close to that of the rule-based systems.

their grammar does not allow for non-binary or partially lexicalized rules (e.g.


We can therefore apply standard parsing algorithms to this task.

we have some additional grammar requirements.


With a rulebased system, such a requirement translates to removing a few rules.

a ML-based approach requires a complete retrain.


In Figure 1, the KB graph (only solid edges) is disconnected, thereby making it impossible for PRA to discover any relationship between Alex Rodriguez and World Series.

addition of the two edges with SVO-based lexicalized syntactic edges (e.g., (Alex Rodriguez, plays for, NY Yankees)) restores this inference possibility.


A method of latent embedding of relation instances for sentence-level relation extraction was shown in (Wang et al., 2011).

none of this prior work makes explicit use of the background KBs as we explore in this paper.


OpenIE systems such as Reverb (Etzioni et al., 2011) also extract verb-anchored dependency triples from large text corpus.

to such approaches, we focus on how latent embedding of verbs in such triples can be combined with explicit background knowledge to improve coverage of existing KBs.


In the case study of Section 3, we use POS-based rules as hidden states.

it should be noted that the hidden structures surely do not have to be POS tags.


These methods were applied to not-so-large scale experiments (55 million (M) words for training their BNLMs) (Arsoy et al., 2013).

our method is applied to SMT and can be used to improve a BNLM created from 746 M words by using a CSLM trained from 42 M words.


Actually, a CSLM trained from a smaller corpus can improve the BLEU scores of SMT if it is used in the n-best reranking (Schwenk, 2010;Huang et al., 2013).

we will demonstrate that a BNLM simulating a CSLM can improve the BLEU scores of SMT in the first pass decoding.


The epoch size for MIRA 1 and MIRA 2 is 40, while the one for c-MIRA is 400. c-MIRA runs more epochs, because we update the parameters by much fewer times.

we can implement Line 3∼8 in Algorithm 1 in multi-thread (we use eight threads in the following experiments), which makes our algorithm much faster.


More specifically, CRFs improve the performance on monolingual posts, especially when a single word is tagged in the wrong language.

when the influence of the context is too high, CRFs reduce the performance in bilingual posts.


Entity linking in long text has been well studied in previous works.

few work has focused on short text such as microblog post.


The content of post (2) is highly related to post (1).

to the confusing post (1), the text in post (2) explicitly indicates that the Abbott here refers to the Australian political leader.


Furthermore, we propose a Graph-based Microblog Entity Linking (GMEL) method.

to CEMEL, the extra posts in GMEL are not directly added into the context.


An ideal solution is to expand the context with the posts which contain the same entity.

automatically judging whether a name mention in two documents refers to the same entity, namely cross document coreference, is not trivial.


This has the effect of increasing the weights of features whose likelihood of appearing in a pair of sentences is strongly influenced by the paraphrase relationship between the two sentences.

if p k = q k , then the KL-divergence will be zero, and the feature will be ignored in the matrix factorization.


Taking the unigram feature not as an example, we have p k = [0.66, 0.34] and q k = [0.31, 0.69], for a KL-divergence of 0.25: the likelihood of this word being shared between two sentence is strongly dependent on whether the sentences are paraphrases.

the feature then has p k = [0.33, 0.67] and q k = [0.32, 0.68], for a KL-divergence of 3.9 × 10 −4 .


The PMI based method has achieved promising results.

according to Kanayama's investigation, only 60% co-occurrences in the same window in Web pages reflect the same sentiment orientation (Kanayama and Nasukawa, 2006).


Turney (2002) chose excellent and poor as seed words.

using isolated seed words may cause the bias problem.


For sequential search problems, like left-to-right tagging and parsing, beam search has been successfully combined with perceptron variants that accommodate search errors (Collins and Roark, 2004; Huang et al., 2012).

perceptron training with inexact search is less studied for bottom-up parsing and, more generally, inference over hypergraphs.


For sequential search problems, such as tagging and incremental parsing, beam search coupled with perceptron algorithms that account for potential search errors have been shown to be a powerful combination (Collins and Roark, 2004;Daumé and Marcu, 2005;Zhang and Clark, 2008;Huang et al., 2012).

sequential search algorithms, and in particular left-to-right beam search (Collins and Roark, 2004;Zhang and Clark, 2008), squeeze inference into a very narrow space.


This characterization in turn suggests that predicting whether a connective should be included might be a difficult problem for an NLP system to address, since current-day systems lack the requisite world knowledge and capacity for inference that would be necessary to evaluate the ease with which coherence relations can be established on arbitrary examples.

it is also possible that the decision to include a connective depends in part on stylistic and other types of factors as well, such that there might be predictive information in the kinds of shallow linguistic and textual features that systems do have access to.


Relatedly, Asr and Demberg (2012b) discuss which connectives are the strongest predictors of which relation types.

there is no work of which we are aware that specifically predicts whether connectives should be used or omitted.


In Japanese, zero references often occur and many of them are categorized into zero exophora, in which a referent is not mentioned in the document.

previous studies have focused on only zero endophora, in which a referent explicitly appears.


The author and reader (A/R) of a document have not been used for contextual clues because the A/R rarely appear in the discourse in corpora based on newspaper articles, which are main targets of the previous studies.

in other domain documents such as blog articles and shopping sites, the A/R often appear in the discourse.


They deal with zero exophora by judging that a zero pronoun does not have anaphoricity.

the information of zero pronoun existences is given and thus they did not address zero pronoun detection.


Likewise, the reader is sometimes mentioned as " " (customer) and others.

since such expressions often refer to someone other than the A/R, whether an expression indicates the A/R of a document depends on the context of the document.


From these results, the A/R mentions including "none" can be predicted to accuracies of approximately 80%.

the recalls are not particularly high: the recall of author is 140/258 and the recall of reader is 56/105.


This task is much simpler than modeling a complete dialogue session (e.g., as proposed in Turing test), and probably not enough for real conversation scenario which requires often several rounds of interactions (e.g., automatic question answering system as in (Litman et al., 2000)).

it can shed important light on understanding the complicated mechanism of the interaction between an utterance and its response.


A drawback of this voting procedure is that the final result may not be independent of the voting order, in some cases.

it is assured that the result is consistent, i.e.


is not catastrophic since most of the missed reactions are tagged as continuation, which is still true (only 10% of the reaction relations are mistagged as sameevent).

there is big room for improvement on this point.


The existing machine learning based approaches substantially improve the robustness of scope detection, and have nearly 80% accuracy.

the approaches ignore the availability of the structured syntactic parse information.


Sánchez et al (2010) employed a tree kernel based classifier with CCG structures to identify speculative sentences on Wikipedia dataset.

in Sánchez's approach, not all sentences are covered by the classifier.


Temporal variations of text are usually ignored in NLP applications.

text use changes with time, which can affect many applications.


We see that both the SE kernel and a periodic kernel (PS, see below) give good results.

for extrapolation, the choice of the kernel is paramount.


a uni-modal burst with a steady decrease.

its uncertainty grows with for predictions well into the future.


In this section we demonstrate the usefulness of our method of modelling in an NLP task: predicting the hashtag of a tweet based on its text.

to this classification approach for suggesting a tweet's hashtag, information retrieval methods based on computing similarities between tweets are very hard to scale to large data (Zangerle et al., 2011).


Direct quotations are used for opinion mining and information extraction as they have an easy to extract span and they can be attributed to a speaker with high accuracy.

simply focusing on direct quotations ignores around half of all reported speech, which is in the form of indirect or mixed speech.


The features they used are:  As we explained earlier the word and character edit features capture similarity between many pairs of queries.

they also tend to mis-classify many other pairs especially when the two queries share many words yet have different intents.


In our current implementation we combine these scores by linear combination: Other more sophisticated ways to combine text and behavior evidence are possible, such as jointly learning over both text and behavior features.

we chose to follow the simpler linear approach for interpretability of the results (e.g., by varying the λ parameter).


The maximum size of the tagset equals to the total generative capacity, or 3844 tags.


1 Instead, we exploit lattices, which offer a much richer representation of the decoder output, since they compactly encode an exponential number of translation hypotheses in polynomial space.

n-best lists are typically very redundant, representing only a few combinations of top scoring arcs in the lattice.


Heaps of split-states 4:  for all t in T do Find best final split-state 23: end for 25: return I.MAX() 26: end function Figure 2: Push-forward rescoring with a recurrent neural network language model given a beam-width for language model split-states k, decoder states V , edges E, a start state s and final states T .

a recurrent neural network language model makes much weaker independence assumptions.


Domain adaptation for SMT usually adapts models to an individual specific domain.

it often lacks some correlation among different domains where common knowledge could be shared to improve the overall translation quality.


After each iteration, feature weights from all decoders are collected (line 16-19).

to the original algorithm (Simianer et al., 2012), we only average the generaldomain feature weights w G 1 , .


We do not have theoretically grounded guarantee.

we observed that the BLEU score of our method on DEV data was slightly lower than that in the baseline system, which indicates the out-of-domain features are less over-fitting on the domain-specific DEV data since SOURSE A point begins with a player serving the ball.


(1992) and Federico (1999) explore models for combining foreground and background distributions for the purpose of language modeling, and their approaches are somewhat similar to ours.


The above formulation applies whenever we have access to comparable corpora.

often we have access to comparable documents, such as those given by Wikipedia inter-language links.


Our science and EMEA corpora are certainly different in domain from the OLD-domain parliamentary proceedings, and our success in boosting MT performance with our methods indicates that the Wikipedia comparable corpora that we mined match those domains well.

the subtitles data differs from the OLD-domain parliamentary proceedings in both domain and register.


We experiment with French-English because tuning and test sets are available in several domains for that language pair.

our techniques are directly applicable to other language pairs, including those that are less related.


The size of a Hiero SCFG grammar is typically larger than phrase-based models extracted from the same data creating challenges in rule extraction and decoding time especially for larger datasets .

the LR-decoding algorithm could avoid these shortcomings such as faster time complexity, reduction in the grammar size and the simplified left-to-right language model scoring.


It might appear that the restriction that target-side rules be GNF is a severe restriction on the coverage of possible hypotheses compared to the full set of rules permitted by the Hiero extraction heuristic.

there is some evidence in the literature that discontinuous spans on the source side in translation rules is a lot more useful than discontinuous spans in the target side (which is disallowed in the GNF).


The Algorithm 1 presented earlier does an exhaustive search as it generates all possible partial translations for a given stack that are reachable from the hypotheses in previous stacks.

only a few of these hypotheses are retained, while majority of them are pruned away.


In such a way, we significantly increase the feature coverage on unseen data.

if we allow arbitrary combinations, we can extract a hexalexical feature (4 Chinese + 2 English words) for a local window in Figure 5, which is unlikely to be seen at test time.


Due to the use of query documents, the LSS formulation has some resemblance to document ranking based on learning to rank (Li, 2011;Liu, 2011).

lSS is very different because we turn the problem into a supervised classification problem.


Note that the authors here are in fact userids.

since they are randomly selected from a large number of userids, the probability that two sampled userids belong to the same person is very small.


In the fake review detection research, researchers have manually label fake reviews and reviewers (Yoo and Gretzel 2009;Lim et al., 2010;Wang et al., 2011).

based on the actual fake reviews written using Amazon Mechanical Turk, Ott et al.


The first is that in (Chen et al., 2004).

as we discussed in related work, their approach is not applicable to reviews.


(Tumasjan et al., 2010;Giglietto, 2012;Kim and Park, 2012).

few of these studies have considered measures beyond simple hashtag frequencies, relative mention counts among politicians, and retweet counts.


de 'of', un 'a/one'); a few others are basic grammatical words (ne '[part of] not', et 'and'), or pronouns or verb forms referring to a single person or object (he/she/it), as well as one noun (France).

many female words (11/25) are pronouns or basic verb forms referring to the speaker or a single addressee (je 'I', mon/mes/ma 'my', tu 'you', j'ai 'I have').


Many other k-top words are familiar terms of address for men (lan, abi, karde sim, adam, kanka) or a greeting used mainly between men (eyvallah), suggesting that male users are addressing or discussing men more often than female users are.

9/25 of the k-top female words are pronouns referring to the speaker, a familiar addressee, or a third party (he/she/it), while none of the k-top male words are, suggesting female users are more often talking directly about themselves or to others.


These multimodal LDA models (hereafter, mLDA) have been shown to be qualitatively sensible and highly predictive of several psycholinguistic tasks (Andrews et al., 2009).

prior work using mLDA is limited to two modalities at a time.


Andrew (2006) also shows that semi-Markov CRF makes strictly weaker independence assumptions than linear CRF and so a word-based segmenter using an order-K semi-Markov model is more expressive than a characterbased model using an order-K CRF.


In the PKU corpus, we do not see a gain using either the data combination approach or the relabelling approach compared to the performance of the Char-Segmenter after cotraining, probably because the Sun-Segmenter just modestly improves over the Char-Segmenter under the CEILING condition.

in the CU corpus, where under the CEILING condition the Sun-Segmenter has a much bigger gain over the Char-Segmenter, there is 0.7% improvement by using the data combination approach and 0.8% by using the relabelling approach, and the improvement is statistically significant.


A precise syntacto-semantic analysis of English requires a large detailed lexicon with the possibility of treating multiple tokens as a single meaning-bearing unit, a word-with-spaces.

parsing with such a lexicon, as included in the English Resource Grammar, can be very slow.


Passive-to-active voice transformation in English can be performed systematically, which does not depend on lexical information in most cases.

in Japanese, the method of transformation depends on lexical information.


4 Ideally, one case frame is constructed for each meaning and voice of the target predicate.

since Kawahara and Kurohashi's method is unsupervised, several case frames are actually constructed  : Examples of case frames for " (be pushed down)" and " (push down)."


Since this case was aligned to no-case of the case frame " -2 (hit-2)," the input ga-case was alternated with no-case.

the cases of the other arguments " (bat-de)" and " (head-wo)" were output as they were in the passive sentence.


Other work tries to extract hypernym relations from large-scale encyclopedias like Wikipedia and achieves high precision (Suchanek et al., 2008;Hoffart et al., 2012).

the coverage is limited since there exist many infrequent and new entities that are missing in encyclopedias (Lin et al., 2012).


the entities in industry domain are more uncommon.


If perfect semantic relations were always available, SemRels would be the preferred mode.

this is often not the case and combining the three logic forms yields better performance (Section 4).


The only noticeable exception is LFT-score with sentences from SMTeuroparl.

best results for SMTeuroparl are obtained dropping unbound predicates and using All features.


Costello (2002) investigated the cognitive process that guides people's choice of words when making up a novel nounnoun compound.

we present a datadriven investigation to quantifying creativity in lexical composition.


Several formulae to compute prior polarities starting from posterior polarities scores have been used in the literature.

their performance varies significantly depending on the adopted variant.


That works because typed queries are often fairly precise, and tabular responses are easily skimmed.

spoken queries, and in particular open-domain spoken queries for unrestricted spoken content, pose new challenges that call for new thinking about interaction design.


For example, given a large collection of texts and images related to a specified news event (e.g., East Japan The researches (Goldstein et al., 2000) on automatic multi-document summarization (MDS) have helped a lot when we generate a description for a specific event.

it traditionally exhibits in a very simple style like a "0-dimensional" point.


Feng and Lapata (2010) use visual words to describe visual features and then propose a probabilistic model based on the assumption that images and their co-occurring textual data are generated by mixtures of latent topics.

to the best of our knowledge, no existing research manages to generate a 2-dimensional story map automatically and integrate images and texts into a unified framework at the same time.


The way to reconstruct a more effective layout of the story map requires further study and provides a good research direction in the future.

in this paper we order the sentences/images in each story node according to their authority scores.


The events with short life-cycles prefer a smaller value of σ to dominate the influence from neighbors, as well as the intramodal bias.

long-living events prefer lager σ and more inter-modal bias to get information replenishment from different dates and modality.


We also tried generating the meta features from the training data only, shown as TrainData in Table 9.

the results shows that the parsers performed worse than the baselines.


(2008) and a condensed feature representation.

our approach is much simpler than theirs and we believe that our meta parser can be further improved by combining their methods.


2 is reformulated as where the notion of disruption can be embedded in the term is defined as a probability.

arbitrary scores which do not correspond to probabilities can be used instead as the search for the best path in the graph of possible segmentations makes no use of probability theory.


Moreover, smooth topic shifts can be found, in particular at the beginning of each program with different reports dedicated to the headline.

transcripts significantly differ from written texts: no punctuation signs or capital letters; no sentence structure but rather utterances which are only loosely syntactically motivated; presence of transcription errors which may imply an accentuated lack of word repetitions.


In image processing, researchers have studied the problem of brand identification from image using histogram comparison (Pelisson et al., 2003).

to the best of our knowledge, even though textual data is vastly available, the problems of automatic brand identification from raw text and computational branding analytics, are new.


To solve this problem, graph methods seem to be a good solution, because they are simple, generalizable, and are often used to model such complex dependency structures (Cohen, 2012).

combining the sparse modeling and spectral graphical modeling approaches in a principled way is challenging.


Aligned with previous study in marketing science (Moon and Quelch, 2006), an informative set of features related to Starbucks store decorations showed up in our model: "store", "restroom", "public", "bathroom", and "spacious".

these features stopped to show up on the list of Dunkin' Donuts.


Also, it is possible to consider the higher order n-gram features for better exploratory data analysis.

since the focus of this paper is a proof of concept for Laplacian structured sparsity models and computational branding analytics, we have not yet explored various multiview representations to augment our model.


The Minnesota Multiphasic Personality Inventory, MMPI) are based on norm-referenced self-report, and therefore depend on patients' willingness and ability to report symptoms.

some individuals are motivated to underreport symptoms to avoid negative consequences (e.g.


We also explored including essays' average sentence length and total wordcount, as an initial proxy for language complexity, which often figures into psychological assessments.

results adding these features to LIWC did not differ significantly from LIWC alone, and for brevity we do not report them.


To implement the extraction step, we use Z&N's and K&Z's observation: ZPs can only occur before a VP node in a syntactic parse tree.

according to K&Z, ZPs do not need to be extracted from every VP: if a VP node occurs in a coordinate structure or is modified by an adverbial node, then only its parent VP node needs to be considered.


Like an overt pronoun, a ZP whose closest overt antecedent is far away from it is harder to resolve than one that has a nearby overt antecedent.

a corpus study of our training data reveals that only 55.2% of the AZPs appear in the same sentence as their closest overt antecedent, and 22.7% of the AZPs appear two or more sentences away from their closest overt antecedent.


The smaller vocabulary size allows us to efficiently model larger context, so in addition to the 4-gram LM, we also train a 7-gram LM based on word classes.

to an LM of the same size trained on word identities, the increase in computational resources needed for translation is negligible for the 7-gram word class LM (wcLM).


The strongest degradation can be seen when replacing the TM, while replacing the HRM only leads to a small drop in performance.

when the word class models are added as additional features to the baseline, we observe improvements.


In most of these operations, the examples in a minibatch can be processed in parallel.

in the sparse-dense products used when updating the parameters D and D , we found it was best to divide the vocabulary into blocks (16 per thread) and to process the blocks in parallel.


We assumed above that the words are generated independently from the grammatical relations.

we are likely to ignore valuable information in doing so.


This constrains the utility of many traditional tree kernels in two ways: i) two sentences that are syntactically identical, but have no semantic similarity can receive a high matching score (see Table 1, top) while ii) two sentences with only local syntactic overlap, but high semantic similarity can receive low scores (see Table 1, bottom).

distributional vector representations of words have been successful in capturing finegrained semantics, but lack syntactic knowledge.


Lexical approaches using pairwise semantic similarity of SENNA embeddings (DSM), as well as Wordnet Affective Database-based (WNA) labels perform poorly (Carrillo de Albornoz et al., 2010), showing the importance of syntax for this particular problem.

a syntactic tree kernel (SSTK) that ignores distributional semantic similarity between words, fails as expected.


In addition, WSD systems are not suitable for newly created words, new senses of existing words, or domainspecific words.

wSI systems can learn new senses of words directly from texts because these programs do not rely on a predefined set of senses.


When a system has higher V-Measure and paired F-Score on nouns than another system, it achieves a higher supervised recall on nouns too.


First, most prior work aims to monitor a specific illness, e.g., influenza or food-poisoning by paying attention to a relatively small set of keywords that are directly relevant to the corresponding sickness.

we examine all words people use in online reviews, and draw insights on correlating terms and concepts that may not seem immediately relevant to the hygiene status of restaurants, but nonetheless are predictive of the outcome of the inspections.


2012, which transformed words from different languages to WordNet synset identifiers as interlingual sense-based representations.

multilingual WordNet resources are not always available for different language pairs.


Though MT can greatly increase the test accuracies comparing to the other four methods, TB, CL-Dict, CLD-LSA, and CL-SCL, the benefit is obtained at the cost of whole document translations.

our proposed approach does not require whole document translations, but relies on the same simple word-pair translations used in CL-Dict.


e 7 is a satellite (child) of the nucleus (parent) e 8 .

we cannot judge whether we have to drop e 9 or e 10 because the parent-child relationships are not explicitly defined between e 8 and e 9 , e 8 and e 10 .


He may also issue a query like "travel in Edinburgh" to search relevant questions.

both the browsing and the searching give the user a list of relevant contents (e.g., questions shown in Table 1), not the direct knowledge.


In this paper, given a root topic, subtopics and lower-level topics are extracted from UGC, which form a hierarchical structure to organize corresponding UGC.

in (Zhu et al., 2013) more external sources are utilized to identify subtopics.


Besides, (Singh, 2012b) proposed an entitybased translation language model and demonstrated that it outperformed classical translation language model in question retrieval.

to the best of our knowledge, no previous study leverages entities to organize UGC in social media.


Note that Set EC only covers a small set of real entities and clustering on Set EC is partial clustering.

it leverages Freebase labels and avoids manual labeling, which is time-consuming.


We can find that Stanford NER and FIGER get a relatively high precision in extracting entities.

their recalls are very low and only about 15% of entities are recognized.


The reason is that the CETbased program clusters similar results in the same group, and if the user finds one answer she can easily get more answers.

the list-based program returns a list of questions, and users need to find answers question-by-question.


Intuitively, questions sharing similar topics should be ranked similarly.

traditional question retrieval models (Cao et al., 2010) such as QLLM and VSM do not capture key semantics and give more weights for entity terms.


The number of word types in WEBQUESTIONS is larger than in datasets such as ATIS and GeoQuery (Table 3), making lexical mapping much more challenging.

in terms of structural complexity WEBQUESTIONS is simpler and many questions contain a unary, a binary and an entity.


(Kate and Mooney, 2006;Wong and Mooney, 2007;Muresan, 2011;Kwiatkowski et al., 2010Kwiatkowski et al., , 2011Kwiatkowski et al., , 2012Jones et al., 2012).

these techniques require training data with hand-labeled domain-specific logical expressions.


The model learns to correctly produce complex forms that join multiple relations.

there are a number of systematic error cases, grouped into four categories as seen in Figure 9.


The cohesion model built on these noisy super target lexical chains may select incorrect words rather than the proper lexical chain words.

if we set the threshold too large (e.g., 0.3 or 0.4), we may take the risk of not selecting the appropriate chain word translations into the super target lexical chains.


This suggests that stress has an effect on phoneme alteration, something we discuss in more detail in Section 5.

while providing a large gain in the p2p condition, pronunciation modeling gives small or negative effects elsewhere.


This idea allows the words to be represented by vectors of statistics collected from a sufficiently large corpus of text; each element of the vector reflects how many times a word co-occurs in the same context with another word of the vocabulary.

due to the generative power of natural language, which is able to produce infinite new structures from a finite set of resources (words), no text corpus, regardless of its size, can provide reliable distributional representations for anything longer than single words or perhaps very short phrases consisting of two words; in other words, this technique cannot scale up to the phrase or sentence level.


So in a sense the output element embraces both input elements, resembling a union of the input features.

the elementwise multiplication of two vectors can be seen as the intersection of their features: a zero element in one of the input vectors will eliminate the corresponding feature in the output, no matter how high the other input component was.


An ambiguous vector for 'run' will have non-zero values for every component.

we would expect the vector for 'horse' to have high values for the 'race', 'gallop', and 'move' components, and very low values (but not necessarily zero) for the dissolving-related ones-it is always possible for the word 'horse' to appear in the same context with the word 'painting', for example.


Our second set of experiments is based on the phrase similarity task of Mitchell and Lapata (2010).

with the task of Section 8.1, this one does not involve any assumptions about disambiguation, and thus it seems like a more genuine test of models aiming to provide appropriate phrasal or sentential semantic representations; the only criterion is the degree to which these models correctly evaluate the similarity between pairs of sentences or phrases.


Despite the obvious benefits of the tensor-based approaches, this work suggests for one more time that vector mixture models might constitute a hardto-beat baseline; similar observations have been made, for example, in the comparative study of Blacoe and Lapata (2012).

when trying to interpret the mixing results regarding the effectiveness of the tensor-based models compared to vector mixtures, we need to take into account that the tensorbased models tested in this work were all "hybrid", in the sense that they all involved some element of point-wise operation; in other words, they constituted a trade-off between transformational power and complexity.


As a generalization of LSA, MRLSA is also a linear projection model.

while the words are represented by vectors as well, multiple relations between words are captured separately by matrices.


This confirms our claim that when giving the same among of information, MRLSA performs at least comparably to PILSA.

the true power of MRLSA is its ability to incorporate other semantic relations to boost the performance of the target task.


The standard way of building a bilingual vector space is to use bilingual lexicon entries (Rapp, 1999;Fung and Cheung, 2004;Gaussier et al., 2004) as dimensions of the space.

there seems to be an apparent flaw in logic, since the methods assume that there exist readily available bilingual lexicons that are then used to induce bilingual lexicons!


According to the results from tables 1 and 2, regardless of the seed lexicon size, the bootstrapping approach does not suffer from semantic drift, i.e., if we seed the process with high-quality symmetric translation pairs, it is able to recover more pairs and add them as new dimensions of the bilingual vector space.

we also study the influence of applying different confidence estimation functions on top of the symmetry constraint (see sect 2.3), but we do not observe any improvement in the BLE results, regardless of the actual choice of a confidence estimation function.


Because they cannot capture the meaning of longer phrases properly, compositionality in semantic vector spaces has recently received a lot of attention (Mitchell and Lapata, 2010;Socher et al., 2010;Zanzotto et al., 2010;Yessenalina and Cardie, 2011;Grefenstette et al., 2013).

progress is held back by the current lack of large and labeled compositionality resources and models to accurately capture the underlying phenomena presented in such data.


An alternative to RNTNs would be to make the compositional function more powerful by adding a second neural network layer.

initial experiments showed that it is hard to optimize this model and vector interactions are still more implicit than in the RNTN.


In (1), although there is a positive sentiment, the target of the sentiment is an event (Kentucky losing to Tennessee).

from the positive sentiment toward this event, we can infer that the speaker has a negative sentiment toward Kentucky and a positive sentiment toward Tennessee.


Yang and Cardie (2013) is similar in spirit to our own, where the identification of opinion holders, opinion targets, and opinion expressions is modeled as a sequence tagging problem using a CRF.

similar to previous work ap-plying CRFs to extract sentiment, Yang and Cardie use syntactic relations to connect an opinion target to an opinion expression.


Theoretically, one can directly apply EM to solve the problem (Knight et al., 2006).

eM has time complexity O(N • V 2 e ) and space complexity O( e are the sizes of ciphertext and plaintext vocabularies respectively, and N is the number of cipher bigrams.


Since the deciphering model described by Dou and Knight (2012) does not consider word reordering, it needs to decipher the bigram into "nations united" in order to get the right word translations "naciones"→"nations" and "unidas"→"united".

the English language model used for decipherment is built from English adjacent bigrams, so it strongly disprefers "nations united" and is not likely to produce a sensible decipherment for "naciones unidas".


As in that work, we model morphological features rather than directly inflected forms.

that work may be criticized for providing no mechanism to translate surface forms directly, even when evidence for a direct translation is available in the parallel data.


Similar gains are obtained by model combination of the DT approach with the best Boost model.

a combination of the SMT-based CLIR approaches DT and PSQ barely improved results over the best input model.


Both models adopt a recurrent language model for the generation of the target translation (Mikolov et al., 2010).

to other n-gram approaches, the recurrent language model makes no Markov assumptions about the dependencies of the words in the target sentence.


Timeline construction involves identifying temporal relations between events (Do et al., 2012;Mc-Closky and Manning, 2012;D'Souza and Ng, 2013), and is thus related to process extraction as both focus on event-event relations spanning multiple sentences.

events in processes are tightly coupled in ways that go beyond simple temporal ordering, and these dependencies are central for the process extraction task.


The triggers bind and binds cannot denote the same event if a third trigger secrete is temporally between them.

local predicts they are the same event, as they share a lemma.


1 Their system is fully automatic, domain-independent, and scales to large text corpora.

we identify several limitations in the schemas produced by their system.


produces three tuples: Relational triples provide a more specific representation which is less ambiguous when compared to (subj, verb) or (verb, obj) pairs.

using relational triples also increases sparsity.


Prior work by Chambers and Jurafsky (2008; showed that event sequences (narrative chains) mined from text can be used to induce event schemas in a domain-independent fashion.

our manual evaluation of their output showed key limitations which may limit applicability.


Both latent and generative topic models attempt to find topics from the data and it has been found that in some cases they are equivalent (Ding et al., 2006).

this approach suffers from the problem that the topics might be artifacts of the training data rather than coherent semantic topics.


Furthermore, in the case that W N and X has n non-zeroes, the calculation of the SVD is of complexity O(nN + W N 2 ) and requires O(W N ) bytes of memory.

oNETA requires computation time of o(N a ) for a > 2, which is the complexity of the matrix inversion algorithm 3 , and only o(n + N 2 ) bytes of memory.


order O(N a 1 ), being much more efficient than the eigenvalue methods.

it is potentially more error-prone as it requires that a left-inverse of C exists.


The variance analysis result on the six groups of scores (scores given by five times of five-fold cross-validation and the scores provided by human rater), no significant difference, suggests the robustness of our proposed approach.

although pref-erence ranking based approach, SVM for ranking, and regression based approach, SVM for regression, give very good result in human-machine agreement, their variance analysis results indicate that there exists significant difference between the scores given by human and machine raters.


6 We have proposed a novel Joint, Additive, Sequential (JAS) model of conversational topics and speech acts.

to previous approaches to modeling conversational exchanges, this model factors both the current topic and the current speech act into token emission and state transition probabilities.


If an entity or an entity pair appears significantly more frequently in one day's news than in recent history, the corresponding event candidates are likely to be good to generate paraphrase.

the temporal burstiness heuristic implies that a good EEC (a 1 , a 2 , t) tends to have a spike in the time series of its entities a i , or argument pair (a 1 , a 2 ), on day t. even if we have selected a good EEC for paraphrasing, it is likely that it contains a few relation phrases that are related to (but not synonymous with) the other relations included in the EEC.


A total of 349 training reports and 477 test reports were made available to the participants.

data which came from UPMC (more than 50% data) was not made available for public use.


extended the ILP formulation and used soft constraints within the Constrained Conditional Model formulation (Chang, 2011).

their implementation performed only approximate inference.


Label propagation (Zhu and Ghahramani, 2002) aims to spread label distributions from a small training set throughout the graph.

our unsupervised algorithm leverages the connection between two adjacent unlabeled nodes to find the correct labels for both of them.


The segmentation errors especially on opinion target words will directly influence the results of part-of-speech tagging and candidate extraction.

some of the opinion target words in a topic are often included in the hashtag.


In the topic #90 后打老人# (means "A young man hits an old man"), "90 后" (literally "90 later" and means a young man born in the 90s) is an important word because it is the opinion target of many sentences.

existing Chinese word segmentation tools will regard it as two separate words "90" and "后" ("later").


So far in our model topics and events are not related.

many events are highly related to certain topics.


TimeUserLDA also models topics and events by separating topic tweets from event tweets.

it groups event tweets into a fixed number of bursty topics and then uses a twostate machine in a postprocessing step to identify events from these bursty topics.


Thus, events are not directly modeled within the generative process itself.

events are inherent in our generative model.


This is because this method mixes topics and events first and only detects events from bursty topics in a second stage of postprocessing.

our model performs well for topic-oriented events.


Each warp of threads shares a program counter and executes code in lock-step.

execution is not SIMD -all threads do not execute all instructions.


REG is related to content selection, which has been studied for generating text from databases (Konstas and Lapata, 2012), event streams (Chen et al., 2010), images (Berg et al., 2012;Zitnick and Parikh, 2013), and text (Barzilay and Lapata, 2005;Carenini et al., 2006).

most approaches to this problem output bags of concepts, while we construct full logical expressions, allowing our approach to capture complex relations between attributes.


The RANKBOOST is a boosted decision stump where, in each boosting iteration, the stump is found by maximizing the weighted exponential rank loss.

both the EXPENS and LAMBDAMART make use of tree learners in the ensemble classifier they produce.


If two users rate the same movies with equals ratings, then these similarities will be maximal.

they may have rated identically but for completely different reasons, making them not alike at all.


In replicating Hopkins and May's experiments, we confirm that existing search algorithms for MERT-including coordinate ascent, Powell's algorithm (Powell, 1964), and random direction sets (Cer et al., 2008)-perform poorly in this experimental condition.

when using our gradient-based direction finder, MERT has no problem finding the true optimum even in a 1000-dimensional space.


On the other hand, regularized MERT only requires one hyperparameter to tune: a regularization penalty for 2 or 0 .

since PRO optimizes translation length on the Dev dataset and MERT does so using the Tune set, a comparison of the two systems would yield a discrepancy in length that would be undesirable.


First, unregularized MERT can achieve competitive results with a small set of highly engineered features, but adding a large set of more than 200 features causes MERT to perform poorly, particularly on the test set.

unregularized MERT can recover much of this drop of performance if it is given a good sparse initializer w 0 .


In Section 4, we gave a simple set of features that yielded a high-performance coreference system; this high performance is possible because features targeting only superficial properties in a fine-grained way can actually model complex linguistic constraints.

while our existing features capture syntactic and discourse-level phenomena surprisingly well, they are not effective at capturing semantic phenomena like type compatibility.


To test how much of this performance could be obtained by a simpler iterated network, we experimented with ablated systems that don't fork or join, i.e., our classic "baby steps" schema (chaining together 15 optimizers), using both DBM and DBM 0 , with and without a transform in-between.

all such "linear" networks scored well below 50%.


In this limit, posterior regularization degenerates into the convex log-likelihood objective normally used for supervised data J Q (θ) = L(θ).

in the general case, the PR objective J Q is not necessarily convex.


2012;Lerman and Hogg 2010;Szabo and Huberman 2010), which is closely related to the highlighting task.

to these approaches, we strive to predict what term a user is likely to be interested in when reading content, which may or may not be the same as the most popular content that is related to the current document.


However, exploiting this extra information does not always need to result in a better model, as the target side words are only derived from the given source side, which is available to both TMs and JMs.

including future source words in a bidirectional model clearly improves the performance further.


The ability of formulating persuasive arguments is not only the foundation for convincing an audience of novel ideas but also plays a major role in general decision making and analyzing different stances.

current writing support is limited to feedback about spelling, grammar, or stylistic properties and there is currently no system that provides feedback about written argumentation.


To the best of our knowledge, there is currently only one approach that aims at identifying argumentative discourse structures proposed by Mochales-Palau and Moens (2009).

it relies on a manually created context-free grammar (CFG) and is tailored to the legal domain, which follows a standardized argumentation style.


We are only aware of one approach (Mochales-Palau and Moens, 2009;Wyner et al., 2010) that also focuses on the identification of argumentative discourse structures.

this approach is based on a manually created CFG that is tailored to documents from the legal domain, which follow a standardized argumentation style.


Error analysis: The system performs well for separating argumentative and non-argumentative text units as well as for identifying premises.

the identification of claims and major claims yields lower performance.


It turned out that structural features are the most effective ones for this task.

some of those features are unique to persuasive essays, and it is an open question if there are general structural properties of arguments which can be exploited for separating claims from premises.


This is because in the current version of our system, the confidence estimation of the intention identifier for domain-dependent dialogue queries is less reliable due to the lack of context information.

the confidence scores returned by the domain experts will be more informative at this point.


Given a state, a most straightforward policy is to select the action that corresponds to the maximum mean Q-value estimated by the GP.

since the objective is to learn the Q-function associated with the optimal policy by interacting directly with users, the policy must exhibit some form of stochastic behaviour in order to explore alternatives during the process of learning.


Next, a tree subtraction algorithm was used to extract the arguments.


(2014), we obtained the syntactic category of the connectives from the list provided in Knott (1996).


(Rieser and Schlangen, 2011)) means that repair detection should operate without unnecessary processing overhead, and function efficiently within an incremental framework.

such left-to-right operability on its own is not sufficient: in line with the principle of strong incremental interpretation (Milward, 1991), a repair detector should give the best results possible as early as possible.


This example illustrates that constrained decode reinforces the errors from the baseline.

the training materials for partial-label learning are purely the encoded knowledge, which is not impacted by the baseline model error.


In this research we employ a sausage constraint to encode the knowledge for Chinese word segmentation.

a sausage constraint does not reflect the legal label sequence.


This approach has the advantage of being able to use a partially annotated corpus.

if performance of lexical normalization is crucial, we have to use the standard perceptron algorithm.


For the first utterance, if punctuation prediction is performed first, it might break the utterance both before and after "uh" so that the second-stage disfluency prediction will treat the whole utterance as three sentences, and thus may not be able to detect any disfluency because each one of the three sentences is legitimate on its own.

for the second utterance, if disfluency prediction is performed first, it might mark "I am sorry" as disfluent in the first place and remove it before passing into the second-stage punctuation prediction.


hard cascade has a higher upper-bound than soft cascade.


And we also obtain the same conclusion as  that mixed-label LCRF performs better than isolated prediction.

for the comparison between the 2-layer FCRF and the cross-product LCRF, although the 2-layer FCRF performs better than the cross-product LCRF on disfluency prediction, it does worse on punctuation prediction.


From the above comparisons, we can see that increasing the label granularity can greatly improve the accuracy of a model.

this may also increase the model complexity dramatically, especially when higher clique order is used.


Moreover, the 2-layer FCRF and the cross-product LCRF perform slightly better than the mix-label LCRF and the soft-cascade approach, suggesting that modelling at a finer label granularity is potentially beneficial.

the soft cascade approach is more efficient than the joint approach when a higher clique order is used.


In this work we chose one particular set of features U.

given the large body of research into NLP feature engineering (Jurafsky and Martin, 2009), this class is extensible beyond just this set, which makes it suitable for many other NLP applications.


Conventional word alignment methods allow discontinuous alignment, meaning that a source (or target) word links to several target (or source) words whose positions are discontinuous.

we cannot extract phrase pairs from this kind of alignments as they break the alignment consistency constraint.


They need manually aligned bilingual texts to train the model.

the manually annotated data is too expensive to be available for all languages.


For example, the Chi- nese word "shi 2 " 1 is aligned to the English words "was 4 " and "that 10 ".

these two English words are discontinuous, and we cannot extract the phrase pair "(shi, was)".


From the initial alignment, we can extract a hierarchical phrase pair "(dang X 1 shi, when X 1 )" from the discontinuous alignment of the English word "when".

the hierarchical phrase pair cannot be extracted from our refined alignment, because our method discards the link between the Chinese word "dang" and the English word "when".


(2014) also enforce agreement during decoding.

these agreement models do not take into account the difference in language pairs, which is crucial for linguistically different language pairs, such as Japanese and English: although content words may be aligned with each other by introducing some agreement constraints, function words are difficult to align.


Using these limited resources, it has been shown that taking the translation direction into account when training a statistical machine translation system can improve translation quality (Lembersky et al., 2013).

improving statistical machine translation using translation direction information has been limited by several factors.


Recently, syntactic information has helped significantly to improve statistical machine translation.

the use of syntactic information may have a negative impact on the speed of translation because of the large number of rules, especially when syntax labels are projected from a parser in syntax-augmented machine translation.


At first gloss, it might seem reasonable to perform significance testing in the following manner when an increase in correlation with human assessment is observed: apply a significance test separately to the correlation of each metric with human judgment, with the hope that the newly proposed metric will achieve a significant correlation where the baseline metric does not.

besides the fact that the correlation between almost any document-level metric and human judgment will generally be significantly greater than zero, the logic here is flawed: the fact that one correlation is significantly higher than zero (r(M new , H)) and that of another is not, does not necessarily mean that the difference between the two correlations is significant.


A source word is marked as beginning (ending) boundary if it is the first (last) word of a translation span.

a source span whose first and last words are both boundaries is not always a translation span.


Vaswani and colleagues (2013) propose a method for reducing the training cost of CSLM and apply it to SMT decoder.

they do not show their improvement for decoding speed, and their method is still slower than the n-gram LM.


Our method performs best when all 3 linguistic features described above are taken into account by the SMT system.

we also experimented with different combinations of those features in order to get some insight of the way each feature influences the translation quality.


Ngrams, the core of BLEU, are sparse at the sentence level, and a mismatch for longer ngrams implies that BLEU falls back on shorter ngrams.

mETEOR has a trainable model and incorporates a small, yet wider set of features that are less sparse than ngrams.


Our use of morphological and lexical features overlaps with the AMEANA framework.

we extend our partial matching to a supervised tuning framework for estimating the value of partial credits.


However, the sentence is the 4 th ranked by annotators.

the output of Sys 3 (ranked 1 st by annotators) has only one exact match, but several partial matches when morphological and lexical information are taken into consideration.


Many statistical models for natural language processing exist, including context-based neural networks that (1) model the previously seen context as a latent feature vector, (2) integrate successive words into the context using some learned representation (embedding), and (3) compute output probabilities for incoming words given the context.

brain imaging studies have suggested that during reading, the brain (a) continuously builds a context from the successive words and every time it encounters a word it (b) fetches its properties from memory and (c) integrates it with the previous context with a degree of effort that is inversely proportional to how probable the word is.


Therefore it seems reasonable that the hidden layer is not only related to the activity when the word is on the screen, but also related to the activity before the word is presented, which is the time when the brain is integrating the previous words to build that context.

as the word i and subsequent words are integrated, the context starts diverging from the context of word i (computed before seeing word i).


However, to date, these previous approaches to multi-modal concept learning focus on concrete words such as cat or dog, rather than abstract concepts, such as curiosity or loyalty.

differences between abstract and concrete processing and representation (Paivio, 1991;Hill et al., 2013;Kiela et al., 2014) suggest that conclusions about concrete concept learning may not necessarily hold in the general case.


Our model is also marginally inferior to alternative approaches in learning representations of ab-stract nouns.

in this case, no method improves on the linguistic-only baseline.


When considering the node "is" in the word sequence, it is likely to be corrected into "are" because it appear directly after the plural noun "parents".

by the definition above, the subsequence corresponding to the node "damaged" is "car is damaged by ".


Early grammatical error correction systems use the knowledge engineering approach (Murata and Nagao, 1994;Bond and Ikehara, 1996;Heine, 1998).

manually designed rules usually have exceptions.


A number of recent works have applied modern machine learning techniques to SCF induction, including point-wise co-occurrence of arguments (Debowski, 2009), a Bayesian network model (Lippincott et al., 2012), multi-way tensor factorization (Van de Cruys et al., 2012) and Determinantal Point Processes (DPPs) -based clustering (Reichart and Korhonen, 2013).

all of these systems induce type-level SCF lexicons and, except from the system of (Lippincott et al., 2012) that is not capable of learning traditional SCFs, they all rely on supervised parsers.


The prior for all θ, p(θ), is the product of all Dirichlet distributions over all non-terminals A ∈ N : Since the Dirichlet distribution is conjugate to the Multinomial distribution, which we use to model the likelihood of trees, the conditional posterior of θ A can be updated as follows: which is still a Dirichlet distribution with updated parameter f r (t) + α r for each rule r ∈ R. Gibbs sampler The parameters of the PCFG model can be learned from an annotated corpus by simply counting rules.

parsing cannot be done directly with standard CKY as with standard PCFGs, so we use the Gibbs sampling algorithm presented in Johnson et al.


The edges that are connected to the OOV neighbor "w" have smaller edge weights such as 3, 5, and 26.

the edges that are connected to common words have higher weights.


The external score favors the well known interpretations of common OOV words.

unlike the dictionary based methodologies, our system does not return the corresponding unabbreviated word in the slang dictionary or in the transliteration table directly.


Using only lexSimScore the system achieved an F-measure of 28.24% on the LexNorm1.1 dataset and 38.70% on the Trigram dataset, which shows that lexical similarity alone is not enough for a good normalization system.

the externalScore which is the layer that is more aware of the Internet jargon, along with some social text specific rule based transliterations performs better than expected on both datasets.


For FH, not surprisingly, we could find matches for 99 of the 100 attributes.

for LT, only 31 of the 100 attributes could be found, even under our permissive setting.


Han14, Roller12 and WB11 follow this strategy, using KL divergence in preference to Naive Bayes.

we find that Naive Bayes in conjunction with Dirichlet smoothing (Smucker and Allan, 2006) works at least as well when appropriately tuned.


Currently, an SRL system works as follows: first identify argument candidates and then perform classification for each argument candidate.

this process only focuses on one independent predicate without considering the internal relations of multiple predicates in a sentence.


A noun phrase may be labeled as A0 for a predicate and at the same time, it can be labeled as A1 for another predicate.

there are few cases that a noun phrase is labeled as A0 for a predicate and as AM-ADV for another predicate at the same time.


explored joint syntactic and se-mantic parsing of Chinese to further improve the performance of both syntactic parsing and SRL.

to the best of our knowledge, in the literatures, there is no work related to multipredicate semantic role labeling.


WordNet frames potentially allow a shallow type pruning based on the semantics provided for the clause constituents.

we could solely distinguish people ("somebody") from things ("something"), which is too crude to obtain substantial pruning effects.


With enough training data, one could hope to learn the details of the interactions of various resolutions.

the expense of producing or obtaining supervised training data at multiple resolutions is prohibitive.


We want a tight correspondence because loose, overlapping alignments are not semantically satisfying.

we do not want to under associate: human language makes reference at a variety of levels (the word level, the phrase level, the utterance level, and beyond).


To optimize Equation 2 it is not practical to search the space of possible S, E combinations (this space is combinatorially large).

we can optimize the factored form using dynamic programming.


Commentators often describe facts about players or the weather or previous games which have no extension in the current game.

our system cannot distinguish such language from the language referring to this game.


The Baseline lemma assigns the domain by taking into account every WN Domain associated to each lemma.

the Baseline wsd selects only the WN Domain of sense disambiguated lemmas.


The higher the threshold, the more high-frequency verbs will prevail in the thesauri, for which the WordNet path similarities are higher.

when adopting a relevance filter of keeping the p most relevant contexts for each verb (Figure 1 right), we obtain similar results, but more stable thesauri.


The use of both filtering methods results in thesauri in which the neighbors of target verbs are closer in WordNet and get better scores in TOEFL-like tests.

the fact that filtering contexts with frequency under th removes verbs in the final thesaurus is a drawback, as highlighted in the extrinsic evaluation on the WBST task.


We therefore follow syntax-based SMT custom and use string/string alignment models in aligning our graph/string pairs.

while it is straightforward to convert syntax trees into strings data (by taking yields), it is not obvious how to do this for unordered AMR graph elements.


Thus a common strategy in SRL systems, formulated by Xue and Palmer (2004), is to look for arguments in the ancestors of the predicate and their direct descendants.

in Czech and Japanese data we observe a large portion of paths with two or more descending arcs, which makes it difficult to characterize the syntactic scope in which arguments are found.


We observe that arcfactored models are in fact more restricted, with a drop in accuracy with respect to unrestricted models.

we also observe that our method largely improves the robustness of the arc-factored method when training with a degree of syntactic variability.


This method has to be evaluated with the Kullback-Liebler divergence metric for each topic space.

this process would be time consuming for thousands of representations of a dialogue.


Deceptive reviews detection has attracted significant attention from both business and research communities.

due to the difficulty of human labeling needed for supervised learning, the problem remains to be highly challenging.


Shell-nounhood is a well-established concept in linguistics (Vendler, 1968;Ivanic, 1991;Asher, 1993;Francis, 1994;Schmid, 2000, inter alia).

understanding of shell nouns from a computational linguistics perspective is only in the preliminary stage.


This method applies to broad range of math problems, including multiplication, division, and simultaneous equations, while ARIS only handles arithmetic problems (addition and subtraction).

our empirical results show that for the problems it handles, ARIS is much more robust to diversity in the problem types between the training and test data.


For instance, the path from cat to animal traverses six intermediate nodes, naïvely yielding a prohibitive search depth of 6.

many of these transitions have low weight: for instance f ↑ cat→feline is only 0.37.


At this point, we have a confidence that the given path has not violated strict Natural Logic.

to translate this value into a probability we need to incorporate whether the inference path is confidently valid, or confidently invalid.


Ravi and Knight (2011) apply Bayesian learning to reduce the space complexity.

bayesian decipherment is still very slow with Gibbs sampling (Geman and Geman, 1987).


Previous work has tried to make decipherment scalable (Ravi and Knight, 2011;Dou and Knight, 2012;Nuhn et al., 2013;Ravi, 2013).

all of them are designed for decipherment with either Bayesian inference or beam search.


The first step is straightforward to implement.

it is not trivial to implement the second step.


2 It is common to use these domain-focused models as additional features besides the domain-confused features.

here we are more interested in replacing the domain-confused features rather than complementing them.


(2009) who estimate the former using meta-information over documents as main features.

our work overcomes the mutual dependence of sentence and phrase estimates on one another by training both models in tandem.


If the model score of each translation is taken to be the sum of rule scores independently given to each rule, the search for the optimal translation is easy with some classic dynamic programming techniques.

if the model score is going to take into account informations such as the language model score of each sentence, it cannot be expressed in such a way.


The alignment (and the linguistic structure of the phrase in the case of Syntax-Based Machine Translation) is then used to produce the target-side rule.

it is often the case that it is difficult to fully specify a rule from an example.


It is interesting to note that, on "fairground" comparison, that is if our decoder do not have the benefit of a more compact latticerule representation, it actually perform quite worse as we can see by comparing with the third column of table 1 (at least in term of decoding time and memory usage, while it would still have a very slight edge in term of model score with the selected settings).

the K-decoder is a rather strong baseline, shown to perform several times faster than a previous state-of-the-art implementation in (Heafield et al., 2013).


The merit of their approach is that they can apply minimization globally, allowing for more possibilities for vertex merging.

for large grammars, the "top-level lattice" will be huge, creating the need to prune vertices during the construction.


All of the previously cited approaches either use uniform weights for combination, or select weights based on collection-level information.

as stated previously, numerous studies suggest that certain methods work better on certain queries, collections, languages.


A recent study extends this idea to the cross-lingual case, by learning how to weight each translated word for English-Persian CLIR (Azarbonyad et al., 2013).

we extract translated word weights from diverse and sophisticated translation methods, then learn how to weight each translated structured query, We call this "learning-totranslate" (LTT), which can be formulated as a simpler learning problem.


Instead of weighting, the translations with highest classifier scores were concatenated, yielding statistically significant improvements over using the single-best translation method.

the translation methods explored in this paper are all based on one-best MT systems, making it difficult to draw strong conclusions.


As it can be observed, in both cases the runtime is linear in the number of components.

the SVD computation in the BoS setting is one order of magnitude faster than time performance in the BoW setting.


These KBs, such as FREEBASE (Bollacker et al., 2008) encompass huge ever growing amounts of information and ease open QA by organizing a great variety of answers in a structured format.

the scale and the difficulty for machines to interpret natural language still makes this task a challenging problem.


Replacing C 2 by C 1 induces a large drop in performance because many questions do not have answers thatare directly connected to their inluded entity (not in C 1 ).

using all 2-hops connections as a candidate set is also detrimental, because the larger number of candidates confuses (and slows a lot) our ranking based inference.


We chose a supervised machine learning approach in order to achieve maximum precision.

this problem can also be approached in an unsupervised setting, similar to the method Whitelaw et al.


The model we have just described considers each sentence in a quiz bowl question independently.

previously-heard sentences within the same question contain useful information that we do not want our model to ignore.


The bag of words model guesses Henry Clay, who was also a Secretary of State in the nineteenth century and helped John Quincy Adams get elected to the presidency in a "corrupt bargain".

the model can reason that while Henry Clay was active at the same time and involved in the same political problems of the era, he did not represent the Amistad slaves, nor did he negotiate the Treaty of Ghent.


More recent factoid qa systems incorporate the web and social media into their retrieval systems (Bian et al., 2008).

to these approaches, we place the burden of learning answer types and patterns on the model.


The syntaxbased (grammar formalism) approaches such as Combinatory Categorial Grammar (CCG) may experience errors if a question has grammatical errors.

our bag-of-words model-based approach can handle any question as long as the question contains keywords that can help in understanding it.


The major challenge of this task is to resolve the ambiguity of phrases, and recent work makes use of various kinds of information found in the document to tackle the challenge.

to this body of work, here we focus on the special case of Wikifying Wikipedia articles, instead of general documents.


Generating gold standard summaries is expensive and time-consuming, a problem that persists with cross-language query biased summarization because those summaries must be query biased as well as in a different language from the source documents.

extrinsic metrics measure the quality of summaries at the system level, by looking at overall system performance on downstream tasks (Jing et al, 1998;Tombros and Sanderson, 1998).


We have approached cross-language query biased summarization as a stand-alone problem, treating the CLIR system and document retrieval as a black box.

summaries need to preserve query-salience: summaries should not make it more difficult to positively identify relavant documents.


Our baseline results table (Table 1) shows REAPER outperforming ASRL in a statistically significant manner on all three ROUGE metrics in question.

we can see from the absolute differences in score that very few additional important words were extracted (ROUGE-1) however REAPER showed a significant improvement in the structuring and ordering of those words (ROUGE-2, and ROUGE-L).


Our result (R-2) is statistically significantly (p < 0.05) better than TAC11 Best system, but not statistically (p > 0.05) significantly different from (Li et al., 2013a).

for the grammar and coherence score, our results are statistically significantly (p < 0.05) than (Li et al., 2013a).


In our preliminary experiments, we used the default similarity threshold 0.7, which was found empirically by the MEAD developers for English.

it produced poor results on the Turkish data set.


Our results show that simple fixed-length truncation methods with high limits (such as taking the first 10 letters) improves summarization scores.

to our expectation, using morphological analysis does not enhance Turkish MDS, possibly due to the homogeneousness of the documents in a cluster to be summarized.


KS14 performs worse with tensor-based methods here than in the other vector spaces.

gS11 and NWE, except copy subject for both of them and Frobenius multiplication for NWE, improved over their verb-only baselines.


On large-scale tasks, neural vectors are more successful than the co-occurrence based alternatives.

this study does not reveal whether this is because of their neural nature, or just because they are trained on a larger amount of data.


The choice of compositional operator (tensorbased or a simple point-wise operation) depends strongly on the task and dataset: tensor-based composition performed best with the verb disambiguation task, where the verb senses depend strongly on the arguments of the verb.

it seems to depend less on the nature of the vectors itself: in the disambiguation task, tensor-based composition proved best for both co-occurrencebased and neural vectors; in the sentence similarity task, where point-wise operators proved best, this was again true across vector spaces.


Experimental results also verify the success of the cube activation function empirically (see more comparisons in Section 4).

the expressive power of this activation function is still open to investigate theoretically.


Figure 6 gives the visualization of three sampled features, and it exhibits many interesting phenomena: • Different features have varied distributions of the weights.

most of the discriminative weights come from W t 1 (the middle zone in Figure 6), and this further justifies the importance of POS tags in dependency parsing.


Motivated by this observation, we hypothesize that the reasons mentioned in the preceding post could be useful for predicting the reasons in the current post.

none of the models we have presented so far makes use of the reasons predicted for the preceding post.


Specifically, in 51-54% of the erroneous cases, a reason sentence is misclassified as NONE.

23-30% of the cases are concerned with assigning a reason label to a NONE sentence.


Yeh and Chen (2007) hand-engineered a set of rules for ZP resolution based on Centering Theory (Grosz et al., 1995).

virtually all recent approaches to this task are based on supervised learning.


We attribute this to the fact that almost all candidate antecedents are singular.

when we ablate any of the remaining three attributes, performance drops significantly by 2.3−3.0% in overall F-score.


We will compare against our implementation of F&S, adapted to English.

unlike F&S or other previous approaches to sentence fusion, the sentence enhancement algorithm may also avail itself of the dependency parses of all of the other sentences in the source text, which expands the range of possible sentences that may be produced.


We find that for a good template like "holiday in [country]", we can often find at least one cluster (one of the country clusters in this example) which has hypernym h and also contains many elements in V .

for invalid templates like "holiday of [book]", every cluster having hypernym h (="book" here) only contains a few elements in V .


For example, given two triples R(Al-Qaeda, attack, American) and R(Terrorist group, attack, American), a taxonomic relation Terrorist group ≫ Al-Qaeda can be induced.

it is not always guaranteed to induce a taxonomic relations from such a pair of triples, for example from R(animal, eat, meat) and R(animal, eat, grass).


Referential taxonomy structures such as WordNet or OpenCyc are widely used in semantic analytics applications.

their coverage is limited to common well-known areas, and many specific domains like Terrorism and AI are not well covered in those structures.


Min and Grishman categorise the slot fills found by human annotators but not found in the aggregated output of all systems.

this approach only allows them to hypothesise the likely source of recall loss.


Using COREF NNP as the sentence filter loses 2% recall, to an upper bound of 78%, for a 12% reduction in the search space.

using a full coreference system generates may more candidates than using simple NNP coreference.


To recover this recall, the strictness of this filter could be relaxed by further generalising dependency paths or using a different similarity metric to direct match of paths.

this is the upper bound for approaches which consider only exact dependency paths as a feature.


We note that limiting bootstrap to one or two iterations is ideal for the best trade-off between recall and search space.

closer analysis of discriminative paths is required for a full SF system.


More recently, researchers have investigated joint inference techniques for event extraction using Markov Logic Networks (MLNs) (e.g., Poon and Domingos (2007), Poon and Vanderwende (2010), Riedel and McCallum (2011a)), a statistical relational model that enables us to model the dependencies between different instances of a data sample.

it is extremely challenging to make joint inference using MLNs work well in practice (Poon and Domingos, 2007).


On the BioNLP'11 and BioNLP'09 datasets, our scores are slightly better and slightly worse respectively than the best reported results.

they are significantly better than state-of-the-art MLNbased systems.


The resulting model can learn that some sequences of syllables (in particular, sequences that start with a stressed syllable) are more likely than others.

observed stress improved token f-score by only 1%.


Together, the experimental and computational results suggest that infants in fact pay attention to stress, and that stress carries useful information for segmenting words in running speech.

stress identification is itself a non-trivial task, as stress has many highly variable, contextsensitive, and optional phonetic reflexes.


This first experiment shows that observing dictionary stress is better early in learning, but that modeling syllable weight is better later in learning.

it is possible that syllable weight was more useful because modeling syllable weight involves modeling the characteristics of codas; the advantage may not have been due to weight per se but due to having learned something about the effects of suffixes on final codas.


Following normal practice, we evaluate our model and compare it with state-of-the-art systems using F-Score.

we argue that the ability to solve segmentation ambiguities is also important when evaluating different types of unsupervised word segmentation systems.


This is because once the goodness measure is given, the decoding algorithm will segment any ambiguous strings into the same word sequences, no matter what their context is.

nonparametric Bayesian language models aim to segment character string into a "reasonable" sentence according to the posterior probability.


This is an essential attribute of Gibbs sampling.

we believe that initializing the Gibbs sampler with the result of nVBE will benefit us in two ways.


Previous unsupervised work usually evaluated their models using F-score, regardless of goodness measure based model or nonparametric Bayesian model.

segmentation ambiguity is a very important factor influencing accuracy of Chinese word segmentation systems (Huang and Zhao, 2007).


There has been work on making use of both unlabeled data (Sun and Xu, 2011;Wang et al., 2011) and Wikipedia (Jiang et al., 2013) to improve segmentation.

no empirical results have been reported on a unified approach to deal with different types of free data.


Liu and Zhang (2012)  study domain adaptation using an unsupervised self-training method.

to their work, we make use of not only unlabeled data, but also leverage any free annotation to achieve better results for domain adaptation.


CRF has been used for Chinese word segmentation (Tseng, 2005;Shi and Wang, 2007;Zhao and Kit, 2008;Wang et al., 2011).

most previous work train a CRF by using full annotation only.


Several other variants of CRF model has been proposed in the machine learning literature, such as the generalized expectation method (Mann and McCallum, 2008), which introduce knowledge by incorporating a manually annotated feature distribution into the regularizer, and the JESS-CM (Suzuki and Isozaki, 2008), which use a EM-like method to iteratively optimize the parameter on both the annotated data and unlabeled data.

we directly incorporate the likelihood of partial annotation into the objective function.


Clearly, bad segmentations translate into poor ATWV scores, as in the case of random and unsupervised segmentations.

gains on segmentation accuracy do not always result in better KWS performance.


Supervised POS tagging has achieved great success, reaching as high as 95% accuracy for many languages (Petrov et al., 2012).

supervised techniques need manually annotated data, and this is either lacking or limited in most resourcepoor languages.


The method we propose in this paper is similar in only using a small amount of annotation.

we directly use the annotated data to train the model rather than using a dictionary.


The 8 languages we are considering in this experiment are not actually resource-poor languages.

running on these 8 languages makes our system comparable with previously proposed methods.


The idea of using the universal tagset is of great use in multilingual applications, enabling comparison across languages.

the mapping is not always straightforward.


The "No LP" model of Das and Petrov (2011), which only uses directly projected labels (without label propagation), scored 81.3% for 8 languages.

using the same model but with more parallel data, Täckström et al.


Plank and van Noord (2008) concluded that this method for adding prior knowledge only works with high quality reference distributions, otherwise performance suffers.

to these previous approaches, we consider the specific setting where both the learned model and the reference model s o = P (t|w) are both maximum entropy models.


In general, because we don't explicitly do any mapping between languages, we might have trouble if the tagset size of the target language is bigger than the source language tagset.

this is not the case for our experiment because we choose English as the source-side and English has the full 12 tags.


In this case the correction model would include a regularization term over the λ to bias towards the DPM parameters, while γ and α would use a zero-mean regularizer.


The introduction of dynamic oracles has considerably improved the accuracy of greedy transition-based dependency parsers, without sacrificing parsing efficiency.

this enhancement is limited to projective parsing, and dynamic oracles have not yet been implemented for parsers supporting non-projectivity.


A transition-based dependency parser is a nondeterministic device, meaning that a given configuration can be mapped into several configurations by the available transitions.

in several implementations the parser is associated with a discriminative model that, on the basis of some features of the current configuration, always chooses a single transition.


In the case where the node γ[i] belongs to σ, i.e., i < , we assign loss contribution 0 to the entry at the top of the stack.

if γ[i] is in β, i.e., i ≥ , we assign loss contribution 0 to several entries in T [i, i + 1] (line 6) because, at the time γ[i] is shifted, the content of the stack depends on the transitions executed before that point.


In general, parsers may prefer VP reading because a transitive verb followed by a noun object is nor-mally a VP structure.

chinese verbs can also modify nouns without morphological inflection, e.g., 養殖/farming 池/pond.


A conjunctive head-head relation between a verb and a noun is rare.

in the sentence "服 務 設備 都 甚 周到" (Both service and equipment are very thoughtful.


Based on the features discussed in the previous sub-section, we extract prior knowledge from Treebank to design the Vt-N classifier.

the training suffers from the data sparseness problem.


An alternative way to extend world knowledge is to learn from largescale unlabeled data (Wu, 2003;Yu et al., 2008).

the unsupervised approach accumulates errors caused by automatic annotation processes, such as word segmentation, PoS tagging, syntactic parsing, and semantic role assignment.


Since the scoring function refers to the prediction history, Choi and Palmer (2012) uses the gold POS tags, y t−1 1 , to generate training examples, which means they assume all of the past decisions are correct.

this causes error propagation problems, since each state depends on the history of the past decisions.